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] | closed | false | null | [] | null | 4 | 2021-02-15T15:16:52Z | 2021-02-15T19:51:46Z | 2021-02-15T19:24:39Z | CONTRIBUTOR | null | I think it would be useful to have a "only unique value" aggregation method. I've implemented a straw-man version so far to get some feedback before I do any more.
- [x] closes #xxxx N/A
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Idea is that you often get data-frames where some of the columns are constant across a group-by. Usually, this is collapsed using "first" when doing a group-by, but it would be good to have a version that verifies that the columns are constant as well.
For example:
```python
df = pd.DataFrame({"id": [0, 0, 1, 1], "name": ["Alice", "Alice", "Bob", "Bob"], "score": [0, 1, 2, 3]})
pd.concat([
df.groupby("id")["name"].only_unique(),
df.groupby("id")["score"].max(),
], axis=1)
```
```raw
name score
id
0 Alice 0.5
1 Bob 2.5
```
```python
df = pd.DataFrame({"id": [0, 0, 1, 1], "name": ["Alice", "Alice", "Bob", "Charlie"], "score": [0, 1, 2, 3]})
pd.concat([
df.groupby("id")["name"].only_unique(),
df.groupby("id")["score"].max(),
], axis=1)
```
```raw
...
ValueError: Expected exactly one unique item in series, but got 2.
```
I've chosen not to drop NA values, so that the exact null value is maintained, but I'm definitely open to changing it.
```python
df = pd.DataFrame({"id": [0, 0, 1, 1], "name": ["Alice", "Alice", None, None], "score": [0, 1, 2, 3]})
pd.concat([
df.groupby("id")["name"].only_unique(),
df.groupby("id")["score"].max(),
], axis=1)
```
```raw
name score
id
0 Alice 0.5
1 None 2.5
```
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} | 8 | 2021-02-15T15:21:36Z | 2021-04-22T22:34:09Z | 2021-04-22T22:16:51Z | MEMBER | null | ~This is on top of https://github.com/pandas-dev/pandas/pull/39772, so only the last commit needs to be looked at for now: https://github.com/pandas-dev/pandas/pull/39820/commits/bd50388e019c61559c310638186aeea57d2d2d3e~
The actual goal here is to have a version of [`array_ops.py::arithmetic_op`](https://github.com/pandas-dev/pandas/blob/9f792cd90373177bffa0e9f85a14d5d5b30fb289/pandas/core/ops/array_ops.py#L159-L191) that does *not* call `ensure_wrapped_if_datetimelike` on its arrays. Reason: when performing ops column-wise, this becomes unnecessary overhead, and so the goal is to ensure this on a higher level, before calling the `array_op`.
So the experiment in this PR is to actually remove `ensure_wrapped_if_datetimelike` from `arithmetic_op`, and then add it in the places that will call `arithmetic_op`, to see how much changes this needs. This would ensure that we can keep a single shared `arithmetic_op` for use by both ArrayManager and BlockManager. The alternative would be to make a variant of `arithmetic_op` without the `ensure_wrapped_if_datetimelike`. That would require fewer code changes, but would give some duplication around `arithmetic_op` content (but it's not that it is a long function). Thoughts on which approach to prefer?
cc @jbrockmendel | {
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} | 16 | 2021-02-15T16:56:49Z | 2021-04-13T14:32:38Z | 2021-04-13T14:16:42Z | CONTRIBUTOR | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
- [x] update docs for existing highlight functions for consistency
- [x] add a new `props` argument to existing highlight functions.
Will add tests if approved:
Could wrap both methods into one as well, but probably easier api if two?
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} | 1 | 2021-02-15T17:38:42Z | 2021-03-18T16:40:49Z | 2021-02-15T23:02:44Z | MEMBER | null | After moving the coverage build from Travis to GitHub Actions #38344, coverage upload was stopped.
Trying to add it back using action provided by codecov. https://github.com/codecov/codecov-action
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} | 4 | 2021-02-15T18:18:38Z | 2021-05-26T02:18:15Z | 2021-05-26T02:18:15Z | NONE | null | #### Is your feature request related to a problem?
When calling `read_csv` and specifying both `sep` and `delim_whitespace`, or both `delimiter` and `delim_whitespace` I get a `ValueError` in pandas 1.3.0.dev0+713.g9f792cd903. For example:
`df = pd.read_csv("my_data.csv", sep=' ', delim_whitespace=True)`
and
`df = pd.read_csv("my_data.csv", delimiter=' ', delim_whitespace=True)`
give an error. However, when I specify both `sep` and `delimiter`, for example:
`df = pd.read_csv("my_data.csv", sep=' ', delimiter='.')`
`sep` is just silently ignored. I think it would make sense to raise an `ValueError` in this case as well.
Moreover, the error that is raised today gives the message `Specified a delimiter with both sep and delim_whitespace=True; you can only specify one.` regardless of whether I specify `sep` or `delimiter` together with `delim_whitespace`. I think it should be changed to `Specified a delimiter with both delimiter and delim_whitespace=True; you can only specify one.` when `delimiter` is used.
#### Describe the solution you'd like
Raise a `ValueError` when both `sep` and `delimiter` are used to specify the separator for `read_csv`.
Change the message "Specified a delimiter with both *sep* and delim_whitespace=True; you can only specify one." to "Specified a delimiter with both *delimiter* and delim_whitespace=True; you can only specify one." when both `delimiter` and `delim_whitespace` are specified.
#### API breaking implications
This will "break" code that specify both `sep` and `delimiter`. However, it is consistent with the behavior when you specify one of those parameters together with `delim_whitespace`. Moreover, a similar change has been done at some time between pandas 0.25.3 (the latest version provided by aptitude) and the development version. In the former
`pd.read_csv("my_data.csv", delim_whitespace=True, sep=',')`
doesn't cause a `ValueError`, but in the latter it does.
#### Describe alternatives you've considered
An alternative could be to issue a warning instead of an error, but an error is more consistent with the current behavior for the combination of `delim_whitespace` and (`sep` or `delimiter`).
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https://api.github.com/repos/pandas-dev/pandas/issues/39824 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39824/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39824/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39824/events | https://github.com/pandas-dev/pandas/pull/39824 | 808,754,400 | MDExOlB1bGxSZXF1ZXN0NTczNzM5NTc4 | 39,824 | REF: remove unnecessary Block attrs | {
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} | 2 | 2021-02-15T18:52:09Z | 2021-02-16T17:55:16Z | 2021-02-16T16:52:18Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 3 | 2021-02-15T19:19:54Z | 2021-02-17T07:09:29Z | 2021-02-16T22:55:57Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/39772
Using `np.prod` gives quite some overhead, and I *think* it's always equivalent to `.size` ?
```
In [1]: arr = np.random.randn(3, 2)
In [2]: %timeit arr.size
42.8 ns ± 0.628 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
In [3]: %timeit np.prod(arr.shape)
6.63 µs ± 344 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
``` | {
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} | 2 | 2021-02-16T02:34:46Z | 2021-02-16T18:51:26Z | 2021-02-16T18:49:33Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 10 | 2021-02-16T04:03:36Z | 2021-02-16T23:34:50Z | 2021-02-16T23:26:34Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 5 | 2021-02-16T04:14:24Z | 2021-02-25T15:10:20Z | 2021-02-25T15:10:19Z | MEMBER | null | - [Thursday, February 25th at 9-10am US Eastern Time](https://everytimezone.com/s/f469edb9)
- [Zoom link](https://nyu.zoom.us/j/98201283174?pwd=d0VoUy9GTDRlVVZ0cCtTK0s4NnlBZz09)
- [Agenda/notes](https://docs.google.com/document/d/1JoiqTdK6gi5W1QiTWrqtTV-rNmIqU3xROgHY5iT4J5g/edit#heading=h.1obsyhexqytc)
---
~~Please [**provide your availability**](https://doodle.com/poll/inay6zfys98t49sv?utm_source=poll&utm_medium=link) in the next few days so we can find a time that works for the most people. Will comment with the date and Zoom link once finalized.~~ All are welcome.
We just kicked off HackIllinois where I'm mentoring students as they contribute to pandas documentation (https://github.com/pandas-dev/pandas/issues/39349), so hoping they can make it to the meeting to see the glamorous side of open source: governance! 😉
[Agenda/notes doc](https://docs.google.com/document/d/1JoiqTdK6gi5W1QiTWrqtTV-rNmIqU3xROgHY5iT4J5g/edit#heading=h.1obsyhexqytc) — feel free to add things in advance. Thanks!
- [x] Start notes doc section
- [x] Finalize time
- [x] Add to calendar
- [x] Send invite to students
@pandas-dev/pandas-core @pandas-dev/pandas-triage @pandas-dev/pydata-sphinx-theme | {
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] | open | false | null | [] | null | 0 | 2021-02-16T05:31:24Z | 2021-08-15T18:13:14Z | null | MEMBER | null | Currently, we are using a vendered version of [pyperclip](https://github.com/asweigart/pyperclip). This project was dead for a couple of years. And there were some updates in 2020. But, it's not under active development right now.
Recently I found this project [pyclip](https://github.com/spyoungtech/pyclip). It looks well maintained and has identical functionalities as pyperclip does. We should investigate if we could replace the vendered pyperclip with pyclip. | {
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] | closed | false | null | [] | null | 6 | 2021-02-16T06:16:21Z | 2021-02-17T10:14:26Z | 2021-02-17T04:38:29Z | MEMBER | null | - [x] I have checked that this issue has not already been reported.
- [ ] ~~I have confirmed this bug exists on the latest version of pandas.~~
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Problem description
This issue is a bit vague, because I'm not sure that I completely understand the problem. And/or it's multiple problems. Anyway:
I have opened a handful of pull requests on pandas recently, and it seems that each time I do (or when I rebase), tests fail for a different reason…unrelated to my change. In https://github.com/pandas-dev/pandas/pull/39364, for example, the build was failing because of:
1. A failing `ipython` cell run back in the `whatsnew` entry for v0.8.0 (fixed in https://github.com/pandas-dev/pandas/pull/39801, then
2. An `AttributeError: 'float' object has no attribute 'value'` error
- [GitHub Actions run](https://github.com/pandas-dev/pandas/actions/runs/570400199)
- [Azure Pipeline run](https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=54282&view=logs&j=b516280a-8c75-541b-8ba0-ae13561718a7&t=88a240e0-0f0f-5fd5-6f10-d82e025ed5d5&l=288)
Anyway, the point is not those specific errors, but that the pandas build is relatively fragile. Upstream issues cause builds for pull requests to fail with errors that have nothing to do with the change, which is confusing for people trying to contribute. This could be because:
- Changes that break the build get merged
- Builds are flaky
- ?
Some ideas of how to help:
- Enable [branch protection](https://docs.github.com/en/github/administering-a-repository/about-protected-branches) on `master`
- Make the continuous integration runs conditional, to only the part of the repository changed
- Do some deeper analysis on what tests are failing most often to see if they are flaky, etc.
Curious to hear ideas from maintainers about if there are patterns of things going on that could be fixed. I imagine it makes sense to split more actionable issues and close this one after some discussion; defer to maintainers on that as well. Thanks! | {
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] | closed | false | null | [] | null | 8 | 2021-02-16T07:19:15Z | 2021-06-18T01:36:13Z | 2021-06-18T01:36:13Z | MEMBER | null | Use a third-party GitHub Action to handle the claiming of issues. [See it in action.](https://github.com/afeld/pandas/issues/2#issuecomment-779635007)
It also handles automatically unassigning them after a period of inactivity, which is the more interesting part. The motivation here is that I bet there are a lot of issues where someone has used `take`, but the issue remains open without movement. [By default](https://github.com/JasonEtco/slash-assign-action#options), this Action will remove the assignment after 21 days. Open to suggestions about whether that should be longer or shorter.
- [ ] ~~closes #xxxx~~
- [ ] ~~tests added / passed~~
- [ ] ~~Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them~~
- [ ] ~~whatsnew entry~~
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} | 6 | 2021-02-16T09:03:28Z | 2021-03-23T15:17:56Z | 2021-03-23T15:17:56Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
csv_reader = pandas.read_csv(
csv_file, iterator=True, chunksize=2500, sep=',', encoding=None, error_bad_lines=False
)
for i, chunk in enumerate(csv_reader):
rows = chunk.to_json(orient="records", lines=True)
```
#### Problem description
**on pypy 7.3.3 - python 3.7.9**
`to_json` execution raises an exception:
```
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/opt/pypy/site-packages/pandas/core/generic.py", line 2478, in to_json
storage_options=storage_options,
File "/opt/pypy/site-packages/pandas/io/json/_json.py", line 94, in to_json
indent=indent,
File "/opt/pypy/site-packages/pandas/io/json/_json.py", line 155, in write
indent=self.indent,
AttributeError: 'BlockManager' object has no attribute 'is_mixed_type'
```
I suspect this PR: https://github.com/pandas-dev/pandas/pull/36873/files
@jbrockmendel do you mind explaining why the `BlockManager` no longer has the `is_mixed_type` property?
#### Expected Output
When I try to manually assign `is_mixed_type` to the `chunk._mgr` object, as dropped in the aforementioned PR (a `BlockManager`) it seems to be working fine:
```
for i, chunk in enumerate(csv_reader):
chunk._mgr._consolidate_inplace()
chunk._mgr.is_mixed_type = len(chunk._mgr.blocks) > 1
print(chunk.to_json(orient="records", lines=True))
{"@timestamp":10000000,"test_1":"something","test_2":"something2"}
```
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.121-linuxkit
Version : #1 SMP Tue Dec 1 17:50:32 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.4
setuptools : 53.0.0
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.23
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/39838 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39838/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39838/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39838/events | https://github.com/pandas-dev/pandas/issues/39838 | 809,149,307 | MDU6SXNzdWU4MDkxNDkzMDc= | 39,838 | BUG: Problems round-tripping categoricals to/from parquet | {
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] | closed | false | null | [] | null | 2 | 2021-02-16T09:43:07Z | 2021-08-15T18:18:20Z | 2021-08-15T18:18:20Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import os
import pandas as pd
fname = 'categorical_test'
data = {
'int': pd.Series([0, 1] * 1000, dtype=pd.CategoricalDtype([0,1])),
'str': pd.Series(['foo', 'bar'] * 1000, dtype=pd.CategoricalDtype(['foo', 'bar'])),
}
df = pd.DataFrame(data)
for write in ['fastparquet', 'pyarrow']:
for read in ['fastparquet', 'pyarrow']:
if os.path.exists(fname):
os.remove(fname)
df.to_parquet(fname, engine=write, compression=None)
df_read = pd.read_parquet(fname, engine=read)
print()
print('write:', write, 'read:', read)
for t in data.keys():
print(t, df[t].dtype == df_read[t].dtype)
```
#### Problem description
Pandas does not reliably round-trip categorical columns to/from parquet.
This problem was originally reported on the pyarrow bug tracker by @chrisroat in https://github.com/apache/arrow/issues/1688#issuecomment-751128963. I appears that it was not reported to pandas though.
Output from the test-case above:
```
write: fastparquet read: fastparquet
int True
str True
write: fastparquet read: pyarrow
int False
str False
write: pyarrow read: fastparquet
int True
str True
write: pyarrow read: pyarrow
int False
str True
```
#### Expected Output
```
write: fastparquet read: fastparquet
int True
str True
write: fastparquet read: pyarrow
int True
str True
write: pyarrow read: fastparquet
int True
str True
write: pyarrow read: pyarrow
int True
str True
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Germany.1252
pandas : 1.2.1
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.5.0
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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] | closed | false | null | [] | null | 3 | 2021-02-16T10:01:55Z | 2021-08-15T18:20:15Z | 2021-08-15T18:20:15Z | NONE | null | This code reproduces the error:
```python
import pandas as pd
df = pd.DataFrame([{'publish_time': pd.Timestamp('2017-11-13 17:13:01+0000', tz='UTC'),
'title': 'A'},
{'publish_time': pd.Timestamp('2017-11-13 07:30:00+0000', tz='UTC'),
'title': 'B'},
{'publish_time': pd.Timestamp('2017-11-12 19:05:24+0000', tz='UTC'),
'title': 'C'},
{'publish_time': pd.Timestamp('2017-11-13 11:00:04+0000', tz='UTC'),
'title': 'D'},
{'publish_time': pd.Timestamp('2017-11-12 18:01:41+0000', tz='UTC'),
'title': 'E'}])
# This works
print(df['publish_time'].dtype == df['title'].dtype)
# This throws an error
print(df['title'].dtype == df['publish_time'].dtype)
```
```
Traceback (most recent call last):
File "C:\Python38\lib\site-packages\IPython\core\interactiveshell.py", line 3418, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-25-1048e1cb1cf5>", line 1, in <module>
print(df['title'].dtype == df['publish_time'].dtype)
TypeError: Cannot interpret 'datetime64[ns, UTC]' as a data type
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : AMD64 Family 25 Model 33 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.2
numpy : 1.19.3
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 2 | 2021-02-16T10:55:40Z | 2021-02-16T18:03:40Z | 2021-02-16T13:51:36Z | CONTRIBUTOR | null | test was ignored previously cause `tz` not supported. was fixed by PR #35973
might as well include the test now.
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} | 2 | 2021-02-16T13:53:12Z | 2021-02-16T15:34:26Z | 2021-02-16T15:34:26Z | NONE | null | Backport PR #39795: fix benchmark failure with numpy 1.20+ | {
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- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
import timeit
df = pd.DataFrame(index=range(10000))
# Create empty float column by assigning np.nan
print(timeit.timeit(
"df['float_col'] = np.nan",
number=10,
globals=globals()
))
# Create empty datetime64[ns] column by assigning pd.NaT
print(timeit.timeit(
"df['datetime_col_no_typing'] = pd.NaT",
number=10,
globals=globals()
))
# Create empty datetime64[ns] column by instantiating a pd.Series
print(timeit.timeit(
"df['datetime_col_with_typing'] = pd.Series(dtype='datetime64[ns]')",
number=10,
globals=globals()
))
```
#### Output:
```
0.003220411000000034
4.237677962999999
0.017725253000000052
```
#### Problem description
This is a performance issue : the creation of an empty column of type datetime64[ns] is very slow when instantiating it with `pd.NaT`.
I have tracked the problem down to the `should_cache` method in `pandas/core/tools/datetimes.py`, where more than 99% of the time is spent in the `set` function, to determine in the end whether the cache should be used.
If `df[col] = pd.NaT` is indeed a valid way to create a datetime64[ns] column, it should not try to use the cache at all.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Jan 12 22:13:05 PST 2021; root:xnu-6153.141.16~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.19.5
pytz : 2018.5
dateutil : 2.7.3
pip : 21.0.1
setuptools : 49.6.0
Cython : 0.29.21
pytest : 5.3.0
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : 1.0.2
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/39844 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39844/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39844/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39844/events | https://github.com/pandas-dev/pandas/issues/39844 | 809,399,877 | MDU6SXNzdWU4MDkzOTk4Nzc= | 39,844 | CI: series_methods.IsInLongSeriesLookUpDominates.time_isin fails with NumPy 1.20+ | {
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} | 0 | 2021-02-16T15:17:04Z | 2021-07-14T18:19:04Z | 2021-07-14T18:19:04Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/38379#discussion_r567753143, benchmarks skipped in #39795
<details>
```
[ 0.00%] ·· Benchmarking existing-py_home_simon_miniconda3_envs_numpy-types_bin_python
[ 50.00%] ··· series_methods.IsInLongSeriesLookUpDominates.time_isin 16/56 failed
[ 50.00%] ··· ========= =========== ============= =============== =============== =================
-- series_type
--------------------- ---------------------------------------------------------------
dtype MaxNumber random_hits random_misses monotone_hits monotone_misses
========= =========== ============= =============== =============== =================
int64 5 33.5±0ms 38.2±0ms 38.8±0ms 33.8±0ms
int64 1000 31.5±0ms 30.7±0ms 32.0±0ms 88.0±0ms
int32 5 20.3±0ms 20.1±0ms 22.0±0ms 20.6±0ms
int32 1000 28.6±0ms 31.5±0ms 28.8±0ms 79.0±0ms
float64 5 33.3±0ms 31.8±0ms 49.3±0ms 36.9±0ms
float64 1000 103±0ms 161±0ms 67.6±0ms 156±0ms
float32 5 21.0±0ms 20.3±0ms 21.7±0ms 20.1±0ms
float32 1000 91.1±0ms 151±0ms 57.9±0ms 148±0ms
object 5 100±0ms 172±0ms 84.5±0ms 212±0ms
object 1000 149±0ms 60.9±0ms 126±0ms 265±0ms
Int64 5 failed failed failed failed
Int64 1000 failed failed failed failed
Float64 5 failed failed failed failed
Float64 1000 failed failed failed failed
========= =========== ============= =============== =============== =================
[ 50.00%] ···· For parameters: 'Int64', 5, 'random_hits'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Int64' not understood
For parameters: 'Int64', 5, 'random_misses'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Int64' not understood
For parameters: 'Int64', 5, 'monotone_hits'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Int64' not understood
For parameters: 'Int64', 5, 'monotone_misses'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Int64' not understood
For parameters: 'Int64', 1000, 'random_hits'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Int64' not understood
For parameters: 'Int64', 1000, 'random_misses'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Int64' not understood
For parameters: 'Int64', 1000, 'monotone_hits'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Int64' not understood
For parameters: 'Int64', 1000, 'monotone_misses'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Int64' not understood
For parameters: 'Float64', 5, 'random_hits'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Float64' not understood
For parameters: 'Float64', 5, 'random_misses'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Float64' not understood
For parameters: 'Float64', 5, 'monotone_hits'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Float64' not understood
For parameters: 'Float64', 5, 'monotone_misses'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Float64' not understood
For parameters: 'Float64', 1000, 'random_hits'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Float64' not understood
For parameters: 'Float64', 1000, 'random_misses'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Float64' not understood
For parameters: 'Float64', 1000, 'monotone_hits'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Float64' not understood
For parameters: 'Float64', 1000, 'monotone_misses'
Traceback (most recent call last):
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/simon/miniconda3/envs/numpy-types/lib/python3.8/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/simon/pandas/asv_bench/benchmarks/series_methods.py", line 157, in setup
self.values = np.arange(MaxNumber).astype(dtype)
TypeError: data type 'Float64' not understood
```
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https://api.github.com/repos/pandas-dev/pandas/issues/39845 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39845/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39845/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39845/events | https://github.com/pandas-dev/pandas/issues/39845 | 809,471,700 | MDU6SXNzdWU4MDk0NzE3MDA= | 39,845 | DOC: Reindexing behaviour of dataframe column-assignment missing | {
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} | 6 | 2021-02-16T16:45:21Z | 2021-08-09T06:41:55Z | null | NONE | null | #### Location of the documentation
`pandas.core.indexing.IndexingMixin.loc`
`pandas.DataFrame.__setitem__`
#### Documentation problem
When assigning a `Series` through `df[...] = ...` or `df.loc[...] = `, the `Series`' index is expanded to conform to the `DataFrame`'s, and then values are added according to the index:
```python3
In [1]: df
Out[1]:
a
0 1
1 2
2 3
In [2]: df['b'] = pd.Series({1: 'b'})
In [3]: df
Out[3]:
a b
0 1 NaN
1 2 b
2 3 NaN
In [4]: se = pd.Series({2: 'zero', 1: 'one', 0: 'two'})
# NOTE: Order is preserved and reflected in the Series' order
In [5]: se
Out[5]:
2 zero
1 one
0 two
dtype: object
In [6]: df['d'] = se
# NOTE: values have been reordered according to the df's index
In [7]: df
Out[8]:
a b d
0 1 NaN two
1 2 b one
2 3 NaN zero
```
(But in contrast:
```python3
In [4]: df['c'] = {1: 'c'}
<traceback omitted>
ValueError: Length of values (1) does not match length of index (3)
```
)
As far as I can tell, this is not really documented. In the case of `__setitem__` there is *no* API documentation at all, and one is left only with the "Selecting and Indexing Data" guide's examples. In the case of `.loc` there is mention that if using a `Series` as *input*, "The index of the key will be aligned before masking," but this is not what we're doing here. Neither set of examples indicates the behaviour when adding a new column or part of column: the only hints I could find in the guide about [setting with enlargement](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#setting-with-enlargement) added a series whose index was the same as the existing index. This means that it is not clear what order the data will end up in the dataframe and where `NaN`s will be added.
In the case of `.loc` in general the API documentation, although it does exist, is fairly scant. There is a link to the user guide, but personally I think this is pretty important behaviour to document in the reference.
#### Suggested fix for documentation
* Add documentation for `__setitem__` and in particular the behaviour of reindexing `Series`.
* Update documentation for `.loc` to more completely describe the behaviour obtained when *assigning* to `.loc[]`, and include at least one example of assigning to a partial column. Alternatively add this to the user guide. Perhaps something like the following, plus an example like those above:
When assigning a `Series` to a `DataFrame`, either via `.loc` or via the `[]` operator, values of the `Series` will be added to the dataframe according to their index. Values in the `Series` whose label does not appear in the `DataFrame` will not be added, and labels missing from the `Series`' index will be `NaN`. This also means that the order that data appears in the resulting `DataFrame` could be different from the order in the `Series`. | {
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} | 7 | 2021-02-16T16:48:43Z | 2021-02-17T10:16:26Z | 2021-02-16T23:58:27Z | MEMBER | null | I noticed that we currently don't really have a groupby benchmark that specifically targets the cython aggregations. We have of course benchmarks that call those, but eg `GroupByMethods` is setup in such a way that it's mostly benchmarking the factorization and post-processing (also useful of course), while eg for sum only 7% is spent in the actual `groupby_add` algorithm.
So therefore this PR is adding an additional benchmark more targetted at the cython aggregation (where 30-50% of the time is spent in the actual aggregation function, so we will more easily catch regressions/improvements there)
For now I only added "float64". I could also add "float32", but not sure how useful that is (since they are all using fused types, it's the same implementation for both dtypes. | {
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} | 3 | 2021-02-16T19:18:48Z | 2021-02-17T08:39:59Z | 2021-02-17T08:39:31Z | NONE | null | - [ ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
Hello Pandas, while working on cuDF binops, I noticed this edge case:
```python
1 // pd.Series([0], dtype='Int64')
```
```
0 0
dtype: Int64
```
I'm not 100% sure what the right behavior here is, but we're thinking it's maybe to raise. I was wondering if anyone on the pandas side had thought about this and has strong feelings about what the answer _should_ be here. For standard division, casting to float and returning `inf` seems sensible. But with floor division the point is to return an int.
Thanks so much for any thoughts. | {
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} | 3 | 2021-02-16T19:54:43Z | 2021-02-17T21:34:38Z | 2021-02-17T21:34:38Z | MEMBER | null | Currently, we have a benchmark for frame/frame ops with a wide dataframe (originating from previous (long) PR / discussion about this, xref https://github.com/pandas-dev/pandas/pull/32779)
To get a better idea of the trade-offs for future changes to the ops code (eg ArrayManager related), I think it is useful to have this benchmark for a varying rows/columns ratio (it is mostly this ratio that determines how much overhead we have when performing ops column-wise).
So I changed the benchmark to be parametrized over 4 different ratios, from a long to a wide dataframe, and with each case having the same number of total elements in the dataframe.
The shape in the original benchmark was `(500, 2000)`, which is somewhere in between two new cases of `(10_000, 1000)` and `(1000, 10_000)`, so that should preserve the original intent of the benchmark (the last case of `(1000, 10_000)` even has a lower row/column ratio, so should even be a "worse" case for the column-wise ops perspective).
cc @jbrockmendel | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39849 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39849/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39849/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39849/events | https://github.com/pandas-dev/pandas/pull/39849 | 809,655,320 | MDExOlB1bGxSZXF1ZXN0NTc0NDgxMjAw | 39,849 | solved raise an error on redundant definition (#39823) | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 2 | 2021-02-16T21:24:42Z | 2021-07-02T11:30:48Z | null | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame([0.123456789, 1.123456789, 2.123456789], columns=["value"])
df = df.astype({"value": "object"})
print(df.to_string(formatters=["{:.2f}".format]))
```
#### Problem description
This probably has the same root cause as Bug #39336, except it affects object types.
This matters because it is unexpected behavior to the user, and it is possible for Pandas to guess dtypes incorrectly.
Without significant research and finding Bug #39336, I would have never guessed that the way to stop the default float formatter was to do `df = df.astype({"value": float})`.
#### Expected Output
> value
> 0 0.123457
> 1 1.123457
> 2 2.123457
#### Other Information
I am seeing the problem in `pandas.io.formats.format.GenericArrayFormatter._format_strings`.
The `for i, v in enumerate(vals):...` loop should probably adjusted to just use `self.formatter` if it exists and bypass all the other checks. If a user has defined their own formatter, then Pandas should not try to second guess the user based on data type or if it is a NaN value.
For example:
```python
df = pd.DataFrame([None, "Test"], columns=["value"])
print(df.to_string(formatters=[lambda x: "The object is {}".format(type(x))]))
```
Does not do what a user expects.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-65-generic
Version : #73-Ubuntu SMP Mon Jan 18 17:25:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : 0.16.1
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | 2021-02-16T22:16:34Z | 2021-02-17T22:16:26Z | 2021-02-16T23:35:09Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Ref: https://github.com/pandas-dev/pandas/pull/39762#discussion_r575832330 | {
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} | 3 | 2021-02-16T22:38:04Z | 2021-02-17T21:01:57Z | 2021-02-17T20:11:52Z | MEMBER | null | ```
In [3]: arr = np.array(0)
In [4]: arr2 = np.array([0])
In [5]: %timeit is_list_like([])
118 ns ± 2.21 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
328 ns ± 3.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
In [6]: %timeit is_list_like(0)
271 ns ± 5.65 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- PR
348 ns ± 4.17 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
In [7]: %timeit is_list_like(arr)
111 ns ± 1.71 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
369 ns ± 24.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
In [8]: %timeit is_list_like(arr2)
109 ns ± 3.39 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
340 ns ± 1.91 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <-- master
``` | {
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] | open | false | null | [] | null | 6 | 2021-02-16T22:51:52Z | 2021-11-02T00:59:56Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
(note that this requires the gsw library to be installed)
```python
>>> import gsw
>>> import pandas as pd
>>> gsw.SA_from_SP(pd.Series([30,30,30]), pd.Series([1000,1000,1000]), 0, 0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/abarna/.pyenv/versions/gsw/lib/python3.8/site-packages/gsw/_utilities.py", line 62, in wrapper
ret = f(*newargs, **kw)
File "/Users/abarna/.pyenv/versions/gsw/lib/python3.8/site-packages/gsw/_wrapped_ufuncs.py", line 3245, in SA_from_SP
return _gsw_ufuncs.sa_from_sp(SP, p, lon, lat)
File "/Users/abarna/.pyenv/versions/gsw/lib/python3.8/site-packages/pandas/core/generic.py", line 1935, in __array_ufunc__
return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)
File "/Users/abarna/.pyenv/versions/gsw/lib/python3.8/site-packages/pandas/core/arraylike.py", line 284, in array_ufunc
raise NotImplementedError(
NotImplementedError: Cannot apply ufunc <ufunc 'sa_from_sp'> to mixed DataFrame and Series inputs.
```
#### Problem description
My group uses pandas with the [gsw](https://github.com/TEOS-10/GSW-Python) library to do some oceanographic calculations. gsw provides ufuncs to do these calculations. When more than one pd.Series is mixed with the scalar arguments at the end, it results in the NotImplementedError you see above. Also, gsw library itself was only recently updated to defer to the input's `__array_ufunc__` method if it is present.
#### Expected Output
This is the result of some manual broadcasting:
```python
>>> gsw.SA_from_SP(pd.Series([30,30,30]), pd.Series([1000,1000,1000]), pd.Series([0, 0, 0]), pd.Series([0, 0, 0]))
0 30.145599
1 30.145599
2 30.145599
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 5 | 2021-02-16T22:58:37Z | 2021-02-17T20:10:19Z | 2021-02-17T20:10:19Z | CONTRIBUTOR | null | https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=54353&view=logs&j=eab14f69-13b6-5db7-daeb-7b778629410b&t=ce687173-08c6-5301-838d-71b2dda24510
```
2021-02-16T22:44:34.3078267Z =================================== FAILURES ===================================
2021-02-16T22:44:34.3078772Z ___________________ TestTableOrientReader.test_comprehensive ___________________
2021-02-16T22:44:34.3079881Z [gw1] linux -- Python 3.8.5 /home/vsts/miniconda3/envs/pandas-dev/bin/python
2021-02-16T22:44:34.3080157Z
2021-02-16T22:44:34.3080536Z self = <pandas.tests.io.json.test_json_table_schema.TestTableOrientReader object at 0x7f8908515790>
2021-02-16T22:44:34.3080847Z
2021-02-16T22:44:34.3081136Z def test_comprehensive(self):
2021-02-16T22:44:34.3081478Z df = DataFrame(
2021-02-16T22:44:34.3081758Z {
2021-02-16T22:44:34.3082075Z "A": [1, 2, 3, 4],
2021-02-16T22:44:34.3082433Z "B": ["a", "b", "c", "c"],
2021-02-16T22:44:34.3083091Z "C": pd.date_range("2016-01-01", freq="d", periods=4),
2021-02-16T22:44:34.3083753Z # 'D': pd.timedelta_range('1H', periods=4, freq='T'),
2021-02-16T22:44:34.3084228Z "E": pd.Series(pd.Categorical(["a", "b", "c", "c"])),
2021-02-16T22:44:34.3084712Z "F": pd.Series(pd.Categorical(["a", "b", "c", "c"], ordered=True)),
2021-02-16T22:44:34.3085138Z "G": [1.1, 2.2, 3.3, 4.4],
2021-02-16T22:44:34.3085802Z "H": pd.date_range("2016-01-01", freq="d", periods=4, tz="US/Central"),
2021-02-16T22:44:34.3086267Z "I": [True, False, False, True],
2021-02-16T22:44:34.3086608Z },
2021-02-16T22:44:34.3086937Z index=pd.Index(range(4), name="idx"),
2021-02-16T22:44:34.3087246Z )
2021-02-16T22:44:34.3087499Z
2021-02-16T22:44:34.3087786Z > out = df.to_json(orient="table")
2021-02-16T22:44:34.3087971Z
2021-02-16T22:44:34.3088300Z pandas/tests/io/json/test_json_table_schema.py:772:
2021-02-16T22:44:34.3088746Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-02-16T22:44:34.3089152Z pandas/core/generic.py:2499: in to_json
2021-02-16T22:44:34.3089486Z return json.to_json(
2021-02-16T22:44:34.3090018Z pandas/io/json/_json.py:115: in to_json
2021-02-16T22:44:34.3090343Z s = writer(
2021-02-16T22:44:34.3090703Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-02-16T22:44:34.3090961Z
2021-02-16T22:44:34.3091280Z self = <pandas.io.json._json.JSONTableWriter object at 0x7f8908515f40>
2021-02-16T22:44:34.3091597Z
2021-02-16T22:44:34.3091857Z def write(self):
2021-02-16T22:44:34.3092200Z iso_dates = self.date_format == "iso"
2021-02-16T22:44:34.3092519Z > return dumps(
2021-02-16T22:44:34.3092827Z self.obj_to_write,
2021-02-16T22:44:34.3093139Z orient=self.orient,
2021-02-16T22:44:34.3093492Z double_precision=self.double_precision,
2021-02-16T22:44:34.3093853Z ensure_ascii=self.ensure_ascii,
2021-02-16T22:44:34.3094287Z date_unit=self.date_unit,
2021-02-16T22:44:34.3094747Z iso_dates=iso_dates,
2021-02-16T22:44:34.3095093Z default_handler=self.default_handler,
2021-02-16T22:44:34.3095449Z indent=self.indent,
2021-02-16T22:44:34.3095724Z )
2021-02-16T22:44:34.3096244Z E DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.
2021-02-16T22:44:34.3096661Z
2021-02-16T22:44:34.3096974Z pandas/io/json/_json.py:177: DeprecationWarning
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} | 1 | 2021-02-17T03:51:45Z | 2021-02-21T17:23:36Z | 2021-02-21T17:16:21Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 4 | 2021-02-17T05:17:30Z | 2021-05-12T22:14:30Z | 2021-05-12T22:14:30Z | NONE | null | **Code Sample, a copy-pastable example if possible**
`snps_df = pd.read_csv('cast_chr7_snps.txt', sep='\t', header=None)`
[cast_chr7_snps.txt](https://github.com/pandas-dev/pandas/files/5993376/cast_chr7_snps.txt)
**Problem description**
`pd.read_csv` doesn't read the entire file (30219 of the 30423 rows).
I double-checked and don't think there are empty lines in the file. Setting `skip_blank_lines=True` doesn't change the result.
I manually checked some of the lines missed, and they all seem consistent with other lines.
Output of `pd.show_versions()`
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.1
pytz : 2021.1
dateutil : 2.8.1
pip : None
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
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} | 1 | 2021-02-17T05:41:48Z | 2021-02-17T20:10:54Z | 2021-02-17T20:09:31Z | CONTRIBUTOR | null | Please see https://discourse.matplotlib.org/t/canonical-documentation-have-moved/21863
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] | open | false | null | [] | null | 9 | 2021-02-17T06:13:54Z | 2021-08-21T14:48:32Z | null | MEMBER | null | #### Location of the documentation
https://pandas.pydata.org/docs/
#### Documentation problem
I'll write it as a user story:
_As a pandas user, when I come across a small problem with the documentation (like a typo), I want to be able to suggest a fix with minimal friction._
Currently, contributors need to search the repository to find the corresponding source file.
#### Suggested fix for documentation
Having an "Edit this page on GitHub" button would help people contribute to documentation quickly. A couple of resources explaining how to do this with Sphinx:
- https://mg.pov.lt/blog/sphinx-edit-on-github.html
- https://gist.github.com/mgedmin/6052926
Could limit to non-API pages to keep things simple. Possible it makes more sense to do this in [the theme](https://github.com/pydata/pydata-sphinx-theme). | {
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] | closed | false | null | [] | null | 2 | 2021-02-17T08:55:04Z | 2021-02-17T14:38:37Z | 2021-02-17T14:35:48Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
I am reading a big JSON file with json in lines:
`pandas.read_json("big_file.json", chunksize=20, lines=True)`
- There are some extra keys in some lines, so non-existent keys are converted to NaN.
- Dtype is object
- I discovered it when I was trying to save into database because it fails to convert 'NaN' to JSON_ARRAY()
- `dataframe.fillna('value')` is not working with that value
- NaN is considered non-null (text)
```
RangeIndex: 20 entries, 40 to 59
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 t_list 20 non-null object
...
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-65-generic
Version : #73-Ubuntu SMP Mon Jan 18 17:25:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.7.3
pip : 21.0.1
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 1.0.2
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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] | closed | false | null | [] | null | 43 | 2021-02-17T09:10:05Z | 2021-04-26T08:19:54Z | 2021-04-26T08:19:54Z | MEMBER | null | For now, this is just a small example to open discussion about how to tackle this topic (not meant for merging).
Context: for a columnar dataframe (with ArrayManager), we would also perform the groupby operations column by column (instead of on the 2D blocks). Most of our custom cython aggregation algorithms are currently written for 2D arrays (eg `group_add`, `group_mean`, etc. I think only the fill/shift/any/all are 1D). But by being 2D, that means they have some performance penalty when using it for a single column.
Experiment: I copied the implementation for "mean" and made a 1D version of it (exactly the same code, but removing one `for` loop over the columns, and simplifying the indices to index into 1D arrays instead of 2D arrays).
And with that did some timings to see the impact:
```python
N = 1_000_000
df = pd.DataFrame(np.random.randn(N, 10), columns=list("abcdefghij"))
df["key"] = np.random.randint(0, 100, size=N)
# below I am manually doing the aggregation part for one column of the following groupby operation
expected = df.groupby("key").mean()
```
Using the existing 2D algorithm on a single column:
```python
codes, uniques = pd.factorize(df["key"], sort=True)
out_shape = (len(uniques), 1)
out_dtype = "float64"
values = df[['a']]._mgr.blocks[0].values.T
result = np.empty(out_shape, out_dtype)
result.fill(np.nan)
counts = np.zeros(len(uniques), dtype=np.int64)
pd._libs.groupby.group_mean_float64(result, counts, values, codes, min_count=-1)
```
Using the new 1D version:
```python
out_shape1d = (len(uniques),)
out_dtype = "float64"
values1d = df['a']._mgr.blocks[0].values.copy()
result1d = np.empty(out_shape1d, out_dtype)
result1d.fill(np.nan)
counts = np.zeros(len(uniques), dtype=np.int64)
pd._libs.groupby.group_mean_1d_float64(result1d, counts, values1d, codes, min_count=-1)
```
Ensure that both give the same result:
```
In [4]: np.allclose(result.squeeze(), expected['a'].values)
Out[4]: True
In [5]: np.allclose(result1d, expected['a'].values)
Out[5]: True
```
And timings for both:
```
In [8]: %%timeit
...: result = np.empty(out_shape, out_dtype)
...: result.fill(np.nan)
...: counts = np.zeros(len(uniques), dtype=np.int64)
...: pd._libs.groupby.group_mean_float64(result, counts, values, codes, min_count=-1)
4.15 ms ± 54.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [9]: %%timeit
...: result1d = np.empty(out_shape1d, out_dtype)
...: result1d.fill(np.nan)
...: counts = np.zeros(len(uniques), dtype=np.int64)
...: pd._libs.groupby.group_mean_1d_float64(result1d, counts, values1d, codes, min_count=-1)
2.62 ms ± 38.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
So for a single column, the 1D version is ca 30-40% faster (increasing the size of the array with 10x, I also get 40% faster).
Some notes:
- The actual aggregation is only a part of the full groupby operation. But for sufficiently large data, it becomes the most significant portion. E.g. for the `df.groupby("key").mean()` example above, the `group_mean` takes ca 1.5x the time of the factorization step (that's for multiple columns, though, when profiling it for grouping a single column, the ratio becomes the other way around: ca 38% in group_mean and ca 62% in factorization)
- If we want to have an optimized groupby for the ArrayManager-backed dataframe, I think we will need those 1D versions of the cython algos. Of course, what I currently did here would give a huge duplication in code (and also not easily templateable or so) on the short term.
xref https://github.com/pandas-dev/pandas/issues/39146 | {
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] | closed | false | null | [] | null | 48 | 2021-02-17T10:10:15Z | 2021-04-27T07:57:56Z | 2021-04-27T07:57:56Z | NONE | null | I hope it's ok to skip the template as this is not a direct code bug but a website bug.
Sorry for the german locales, but I guess the message is pretty clear anyways. The pandas docs are blocked by google safe browsing service.
Hope this can be clarified quickly as there will be a a lot of support lines in companies running hot ;). | {
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} | 4 | 2021-02-17T16:22:55Z | 2021-02-18T10:46:35Z | 2021-02-17T20:56:47Z | MEMBER | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.lookup.html?highlight=lookup#pandas.DataFrame.lookup
#### Documentation problem
It says
> For an example see lookup() in the user guide.
but the hyperlink doesn't send to the user guide
#### Suggested fix for documentation
link to https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#looking-up-values-by-index-column-labels instead
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 0 | 2021-02-17T17:39:09Z | 2021-02-21T17:23:00Z | 2021-02-21T17:21:14Z | MEMBER | null | small optimization removing ndim kwarg from make_block_same_class | {
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```
d = pd.Timestamp('2017-10-31T00:00:00+00:00')
d -= pd.DateOffset(months=1)
d += pd.DateOffset(months=1)
assert d == pd.Timestamp('2017-10-31T00:00:00+00:00'), f"Date '{d}' is not as expected"
``` | {
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- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Proof of concept for #19254
Is relatively fast compared to @rhshadrach method
```
In [4]: %timeit df.groupby("a").mode()
2.52 ms ± 415 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [5]: %timeit df.groupby("a").mode()
1.99 ms ± 60.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [6]: %timeit gb_mode(df, ['a'], 'b')
3.9 ms ± 310 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [7]: %timeit gb_mode(df, ['a'], 'b')
3.8 ms ± 270 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [8]: %timeit gb_mode(df, ['a'], 'b')
3.89 ms ± 433 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
API for what to do when multimodal needs discussion though. | {
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} | 3 | 2021-02-17T18:01:26Z | 2021-02-17T21:01:30Z | 2021-02-17T20:10:20Z | MEMBER | null | - [x] closes #39854
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
We're already using the same filter in two other places in this file. | {
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} | 5 | 2021-02-17T18:29:39Z | 2021-02-18T10:13:36Z | 2021-02-17T20:56:47Z | CONTRIBUTOR | null | - [x] closes #39863
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} | 0 | 2021-02-17T20:11:52Z | 2021-02-18T09:38:40Z | 2021-02-18T09:38:40Z | NONE | null | Backport PR #39858: DOC: Update matplotlib canonical | {
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] | closed | false | null | [] | null | 5 | 2021-02-17T21:00:27Z | 2021-02-25T10:58:29Z | 2021-02-17T23:01:50Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
np.random.seed(123)
pd.Series(np.random.rand(10)*1e-7).rolling(3).std()
```
#### Problem description
It seems `std()` on a rolling window in pandas 1.2.2 rounds the numbers to exact `0.0` if the result would be `< 1.0e-7`.
In comparison, version 1.1.2 gave the correct result.
#### Expected Output
Output in Pandas 1.1.2
```
Out:
0 NaN
1 NaN
2 3.417240e-08
3 2.321842e-08
4 2.407001e-08
5 6.857074e-09
6 1.861914e-08
7 1.343953e-08
8 2.564385e-08
9 1.901245e-08
dtype: float64
```
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.72-microsoft-standard-WSL2
Version : #1 SMP Wed Oct 28 23:40:43 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 47.1.0
Cython : None
pytest : 5.4.3
hypothesis : 5.20.3
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
#### Actual Output
Output in Pandas 1.2.2
```
Out:
0 NaN
1 NaN
2 0.0
3 0.0
4 0.0
5 0.0
6 0.0
7 0.0
8 0.0
9 0.0
dtype: float64
```
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.72-microsoft-standard-WSL2
Version : #1 SMP Wed Oct 28 23:40:43 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 47.1.0
Cython : None
pytest : 5.4.3
hypothesis : 5.20.3
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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```python
import numpy as np
import pandas as pd
from scipy import sparse as sp_sparse
# Create categorical type and sparse type from it.
custom_type = pd.CategoricalDtype(categories=['Zero', 'One'])
categorical_sparse_type = pd.SparseDtype(dtype=custom_type, fill_value='Zero')
# Create sparse type from string type
string_sparse_type = pd.SparseDtype(dtype='str', fill_value='Zero')
# Dummy Data
data = np.array([['Zero', 'Zero'],
['One', 'Zero']])
# Create sparse data frame from categorical sparse type
categorical_sparse_df = pd.DataFrame(
data=data,
columns=list('AB'),
).astype(categorical_sparse_type)
# Create sparse data frame from string sparse type
string_sparse_df = pd.DataFrame(
data=data,
columns=list('AB'),
).astype(string_sparse_type)
```
The following operation causes an error .
```python
dense_df = categorical_sparse_df.sparse.to_dense()
```
```python-traceback
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-95-364b3ddaf122> in <module>
----> 1 dense_df = categorical_sparse_df.sparse.to_dense()
~/anaconda3/lib/python3.7/site-packages/pandas/core/arrays/sparse/accessor.py in to_dense(self)
302 from pandas import DataFrame
303
--> 304 data = {k: v.array.to_dense() for k, v in self._parent.items()}
305 return DataFrame(data, index=self._parent.index, columns=self._parent.columns)
306
~/anaconda3/lib/python3.7/site-packages/pandas/core/arrays/sparse/accessor.py in <dictcomp>(.0)
302 from pandas import DataFrame
303
--> 304 data = {k: v.array.to_dense() for k, v in self._parent.items()}
305 return DataFrame(data, index=self._parent.index, columns=self._parent.columns)
306
~/anaconda3/lib/python3.7/site-packages/pandas/core/arrays/sparse/array.py in to_dense(self)
1132 arr : NumPy array
1133 """
-> 1134 return np.asarray(self, dtype=self.sp_values.dtype)
1135
1136 _internal_get_values = to_dense
~/anaconda3/lib/python3.7/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
83
84 """
---> 85 return array(a, dtype, copy=False, order=order)
86
87
TypeError: data type not understood
```
In addition, the following does not raise an error, but changes the "Zero"-only column in an unexpected way when groupby is applied.
```python
string_sparse_df.groupby(level=0).apply(lambda x:x)
```
| A | B
-- | -- | --
0 | Zero | Z
1 | One | Z
If the dense version of the data frame is used, the outcome is as expected.
```python
string_sparse_df.sparse.to_dense().groupby(level=0).apply(lambda x:x)
```
| A | B
-- | -- | --
0 | Zero | Zero
1 | One | Zero
#### Problem description
From the description of `pandas.SparseDtype`, my understanding is that the `dtype` argument can be of type `ExtensionDtype`, which is consistent with `CategoricalDtype`. However, doing certain operations (example above) with a sparse data frame of such type causes an `TypeError`.
In addition, replacing the `CategoricalDtype` with a `str` type seems to partially fix the problem. However, it still causes issues with groupby when a column consists of only the `fill_value`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-22-generic
Version : #23~18.04.1-Ubuntu SMP Thu Jun 6 08:37:25 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.2
numpy : 1.18.1
pytz : 2020.4
dateutil : 2.8.1
pip : 21.0.1
setuptools : 46.4.0.post20200518
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.2.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.3.5
tables : 3.5.2
tabulate : None
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.44.1
</details>
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df = pd.DataFrame({
'col1': ['A', 'A', 'A', 'A', 'A', 'A']*15,
'col2': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]*15,
'col3': [0, 1, 9, 4, 2, 3]*15,
'col4': ['a', 'B', 'c', 'D', 'e', 'F']*15
})
df.sort_values('col2', ascending=True)
```
Why this operation disorders the entire dataframe ? The same will be produced if the function will be applied for the col1.
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- Ability to use three-letter month short names as `month` / `startingMonth` arguments of year- / quarter-based offset classes (as already used in their `_from_name` class method).
- Expanded docstrings with, e.g., additional examples.
- Partial refactorization, as a step toward combining `QuarterOffset` and `YearOffset` `__init__` (mentioned as TO DO in the code).
- [ ] xref #38859
- [ ] tests added / passed
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} | 5 | 2021-02-18T03:47:39Z | 2021-08-17T13:13:49Z | 2021-08-17T13:13:49Z | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
(the issue sounds familiar, but I couldn't find it anywhere...)
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> df1 = pd.DataFrame({'a': [1, 2, 3], 'b': [np.nan, 5, 6], 'x': [9, 10, 11]})
>>> df1_ = df1.set_index(['a', 'b'])
>>> df1_
x
a b
1 NaN 9
2 5.0 10
3 6.0 11
>>> df2 = pd.DataFrame({'a': [1, 2, 4], 'b': [np.nan, 5, 7], 'y': [12, 13, 14]})
>>> df2_ = df2.set_index(['a', 'b'])
>>> df2_
y
a b
1 NaN 12
2 5.0 13
4 7.0 14
>>> df1_.combine_first(df2_)
x y
a b
1 NaN 9.0 12.0
NaN 9.0 12.0 <--- duplicated row
2 5.0 10.0 13.0
3 6.0 11.0 NaN
4 7.0 NaN 14.0
```
#### Problem description
When combining two DataFrames with overlapping indices, the indices, which contain `nan` values but are otherwise identical, are duplicated.
The problem doesn't occur when the `nan` values are substituted with say `-1`.
#### Expected Output
```python
>>> df1_.combine_first(df2_)
x y
a b
1 NaN 9.0 12.0
2 5.0 10.0 13.0
3 6.0 11.0 NaN
4 7.0 NaN 14.0
```
It is expected that `combine_first` detects that the index `[1, NaN]` exists in both dataframes and thus does not duplicate this row in the resulting dataframe.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Jan 12 22:13:05 PST 2021; root:xnu-6153.141.16~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.2.2
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.3
setuptools : 46.4.0
Cython : 0.29.21
pytest : 5.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : 1.3.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 1 | 2021-02-18T09:21:54Z | 2021-04-20T22:49:55Z | 2021-04-20T22:49:55Z | NONE | null | #### Problem description
I am expriencing a weird bug while trying to convert a list to datetime.
Here is a bug repro:
```python
import pandas as pd
s = pd.Series([None, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.Timestamp('2016-06-07 20:07:42'), pd.NaT, pd.Timestamp('2016-05-04 20:09:22'), pd.Timestamp('2016-04-12 20:07:40'), pd.Timestamp('2016-03-30 20:10:39'), pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.Timestamp('2015-12-18 20:14:06'), pd.Timestamp('2015-12-15 20:07:59'), pd.Timestamp('2015-11-17 20:09:31'), pd.Timestamp('2015-11-05 20:10:41'), pd.Timestamp('2015-10-29 20:12:12'), pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.Timestamp('2015-07-20 20:10:23'), pd.Timestamp('2015-06-16 20:07:24'), pd.Timestamp('2015-05-28 20:09:39'), pd.NaT, pd.NaT, pd.Timestamp('2015-04-15 20:12:54'), pd.NaT, pd.NaT, pd.NaT, pd.Timestamp('2015-02-20 20:12:07'), pd.Timestamp('2014-12-29 20:04:30'), pd.Timestamp('2014-12-15 20:09:24'), pd.NaT, pd.NaT, pd.Timestamp('2014-11-17 20:03:56'), pd.NaT, pd.Timestamp('2014-10-14 20:04:47'), pd.NaT, pd.NaT, pd.NaT, pd.NaT, pd.Timestamp('2014-08-21 20:06:15'), pd.NaT, pd.Timestamp('2012-07-26 00:10:29')], dtype="object")
pd.to_datetime(s, errors="coerce")
```
What I got is :
```
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
```
Testing on other example, the behavior is working as expected:
```python
import pandas as pd
s = pd.Series([None, pd.NaT, pd.Timestamp('2014-11-17 20:03:56')], dtype="object")
pd.to_datetime(s, errors="coerce")
```
gives me
```
0 NaT
1 NaT
2 2014-11-17 20:03:56
dtype: datetime64[ns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.2.2
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
Info while scrolling a bit with pdb:
```
> /Users/jfournier/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/core/tools/datetimes.py(803)to_datetime()
801 cache_array = _maybe_cache(arg, format, cache, convert_listlike)
802 if not cache_array.empty:
--> 803 result = arg.map(cache_array)
804 else:
805 values = convert_listlike(arg._values, format)
ipdb> cache_array
NaT NaT
NaT NaT
2016-06-07 20:07:42 2016-06-07 20:07:42
2016-05-04 20:09:22 2016-05-04 20:09:22
2016-04-12 20:07:40 2016-04-12 20:07:40
2016-03-30 20:10:39 2016-03-30 20:10:39
2015-12-18 20:14:06 2015-12-18 20:14:06
2015-12-15 20:07:59 2015-12-15 20:07:59
2015-11-17 20:09:31 2015-11-17 20:09:31
2015-11-05 20:10:41 2015-11-05 20:10:41
2015-10-29 20:12:12 2015-10-29 20:12:12
2015-07-20 20:10:23 2015-07-20 20:10:23
2015-06-16 20:07:24 2015-06-16 20:07:24
2015-05-28 20:09:39 2015-05-28 20:09:39
2015-04-15 20:12:54 2015-04-15 20:12:54
2015-02-20 20:12:07 2015-02-20 20:12:07
2014-12-29 20:04:30 2014-12-29 20:04:30
2014-12-15 20:09:24 2014-12-15 20:09:24
2014-11-17 20:03:56 2014-11-17 20:03:56
2014-10-14 20:04:47 2014-10-14 20:04:47
2014-08-21 20:06:15 2014-08-21 20:06:15
2012-07-26 00:10:29 2012-07-26 00:10:29
dtype: datetime64[ns]
```
it seems that there is a deduplication which distinguishes `None` from `NaT`, then `None` got converted to `NaT`, and ultimately the `self.is_unique` check fails as `NaT` is present twice | {
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} | 3 | 2021-02-18T13:24:27Z | 2021-02-19T06:11:37Z | 2021-02-19T01:32:02Z | CONTRIBUTOR | null | `non_reducing_slice` is performed within the `apply` so no need to execute twice.
`maybe_numeric_slice` is a convoluted wrapper for selecting numeric columns so removed.
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This implements one aspect of groupby: basic cython-based aggregations (so not yet general apply, python fallback, or other methods, etc, only the basic aggregations that take the `_cython_agg_general` path).
Similarly to how we currently have a `_cython_agg_blocks`, this PR adds an equivalent `_cython_agg_arrays` which calls the cython operation on each column instead of each block. | {
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- [ ] One test does not pass and I don't know why
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It would address #38914 and #38859.
- [ ] closes #38914 #38859
- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 0 | 2021-02-18T16:46:55Z | 2021-02-21T18:05:08Z | 2021-02-21T18:05:08Z | CONTRIBUTOR | null | The relative luminance calculation is described here: https://www.w3.org/WAI/GL/wiki/Relative_luminance. RL is used by `Styler.background_gradient()` to determine text-color against background but the calculation is wrongly coded.
In addition a new errata posted suggests that a scalar should be changed (albeit this has minimal impact). The greatest change is had through the re-engineered calculation. See below for before and after.
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} | 1 | 2021-02-18T16:50:29Z | 2021-02-22T10:41:18Z | 2021-02-21T18:05:08Z | CONTRIBUTOR | null | - [x] closes #39888
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Addresses #8435 and #5307
- [ ] closes #8435 #5307
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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```
import openpyxl
import pandas as pd
import numpy as np
from openpyxl.workbook import Workbook
wb = openpyxl.load_workbook("Test.xlsx")
wb.create_sheet("Results")
wb.create_sheet("sumPiv")
Results = wb.get_sheet_by_name('Results')
sumPiv = wb.get_sheet_by_name('sumPiv')
Results['A3'] = 'Weekly Average Spend'
Results['A4'] = 'Cost Per Trip'
Results['A5'] = 'Average Trips Per Week'
Results['A6'] = 'Total Spend'
wb.create_sheet('SQLPiv')
df3 = pd.read_excel("Test.xlsx","SQL")
SqlPiv = pd.pivot_table(df3,index = ['Ship_Date_Year' , 'Ship_Date_Weeknum'],
values = ['Customer_Amt_Excl_Fuel'],
aggfunc = ('sum'),
margins = True)
with pd.ExcelWriter('Test.xlsx', engine="openpyxl", mode="a") as writer:
SqlPiv.to_excel(writer, sheet_name="SQLPiv")
df4 = pd.read_excel("Test.xlsx","SQL")
Costpiv = pd.pivot_table(df4,index = ['Ship_Date_Year'],
values = ['Customer_Amt_Excl_Fuel'],
aggfunc = ('sum'),
margins = True)
with pd.ExcelWriter('Test.xlsx', engine="openpyxl", mode="a") as writer:
Costpiv.to_excel(writer, sheet_name="sumPiv")
writer.save()
writer.close()
wb.save('Test.xlsx')
print(wb.get_sheet_names())
print("DONE")
``` | {
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] | open | false | null | [] | null | 1 | 2021-02-18T21:09:13Z | 2021-08-15T18:29:47Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
In [1]: df = pd.DataFrame(dict(a=[nan], b=[0], c=[1]))
In [2]: df
a b c
0 NaN 0 1
# groupby drops groups with null by default
In [3]: df.groupby(['a', 'b']).first()
Empty DataFrame
Columns: [c]
Index: []
# pass dropna=False to keep groups with nulls
In [4]: df.groupby(['a', 'b'], dropna=False).first()
c
a b
NaN 0 1
# This doesn't work if the groups are in a multi index.
In [5]: df.set_index(['a', 'b']).groupby(['a', 'b'], dropna=False).first()
Empty DataFrame
Columns: [c]
Index: []
```
#### Problem description
It seems like setting `dropna=False` in `groupby` doesn't work when the group columns are part of a multi index. I'm not sure if this is intentional, but it is certainly confusing.
#### Expected Output
I would expect the output of [5] above to match that of [4].
#### Output of ``pd.show_versions()``
<details>
```json
{
"system": {
"commit": "7d32926db8f7541c356066dcadabf854487738de",
"python": "3.8.5.final.0",
"python-bits": 64,
"OS": "Linux",
"OS-release": "5.4.0-1035-aws",
"Version": "#37-Ubuntu SMP Wed Jan 6 21:01:57 UTC 2021",
"machine": "x86_64",
"processor": "x86_64",
"byteorder": "little",
"LC_ALL": "C.UTF-8",
"LANG": "C.UTF-8",
"LOCALE": {
"language-code": "en_US",
"encoding": "UTF-8"
}
},
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"numpy": "1.20.1",
"pytz": "2021.1",
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"pip": "20.3.3",
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"hypothesis": null,
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"lxml.etree": null,
"html5lib": null,
"pymysql": null,
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"jinja2": null,
"IPython": "7.20.0",
"pandas_datareader": null,
"bs4": "4.9.3",
"bottleneck": null,
"fsspec": null,
"fastparquet": "0.5.0",
"gcsfs": null,
"matplotlib": null,
"numexpr": null,
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"openpyxl": null,
"pandas_gbq": null,
"pyarrow": "3.0.0",
"pyxlsb": null,
"s3fs": null,
"scipy": null,
"sqlalchemy": null,
"tables": null,
"tabulate": null,
"xarray": null,
"xlrd": null,
"xlwt": null,
"numba": "0.52.0"
}
}
```
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} | 2 | 2021-02-18T21:40:02Z | 2021-04-17T21:37:09Z | 2021-02-28T12:59:59Z | NONE | null | I have this line of code which works fine in pandas 1.1.5 but not in 1.2.2:
`vintable = pd.concat([vintable, data], join='inner', ignore_index=True)`
I expect this to concatenate the rows, inner-join the columns, and then reindex - which it used to do in December. But now I get this error: "Reindexing only valid with uniquely valued Index objects." As I read the docs, it should not be looking at the index at all. So, I am fine sticking with 1.1.5 but I think is a bug ... TIA
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} | 2 | 2021-02-18T22:17:10Z | 2021-02-21T21:52:10Z | 2021-02-21T18:02:11Z | CONTRIBUTOR | null | Add types to `pandas/core/indexes/base.py`.
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] | closed | false | null | [] | null | 6 | 2021-02-18T23:52:45Z | 2021-08-15T18:31:20Z | 2021-08-15T18:31:13Z | NONE | null | I'm trying to read an excel file with pd.read_excel().
The excel file has 2 columns Date and Time and I want to read both columns as str not the excel dtype.
Example of the excel file
[test1.xlsx](https://github.com/pandas-dev/pandas/files/6006593/test1.xlsx)
When using the dtype or the converters arguments the dtype of the columns remain the same
```
df = pd.read_excel('xls_test.xlsx',
dtype={'Date':str,'Time':str})
df.dtypes
Date object
Time object
dtype: object
```
```
df.head()
Date Time
0 2020-03-08 00:00:00 10:00:00
1 2020-03-09 00:00:00 11:00:00
2 2020-03-10 00:00:00 12:00:00
3 2020-03-11 00:00:00 13:00:00
4 2020-03-12 00:00:00 14:00:00
```
Same thing when using converters
```
df = pd.read_excel('xls_test.xlsx',
converters={'Date':str,'Time':str})
df.dtypes
Date object
Time object
dtype: object
```
```
df.head()
Date Time
0 2020-03-08 00:00:00 10:00:00
1 2020-03-09 00:00:00 11:00:00
2 2020-03-10 00:00:00 12:00:00
3 2020-03-11 00:00:00 13:00:00
4 2020-03-12 00:00:00 14:00:00
```
I have also tried to use other engine but the result is always the same.
### pd.show_versions()
```
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 50.3.1.post20201107
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : 1.0.8
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.52.0
```
This seems too obvious to be an unreported bug and I apologise if the problem comes from my side. | {
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] | open | false | null | [] | null | 4 | 2021-02-19T00:53:20Z | 2021-03-31T04:39:59Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
left = pd.DataFrame({'a':[1567808378753000000], 'b':[0]})
right = pd.DataFrame({'a':[1567808378753274000], 'b':[0]})
print(left.compare(right))
```
#### Problem description
The current output is
```
a
self other
0 1567808378752999936 1567808378753274112
```
which differs from the values in the original dataframe.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
a
self other
0 1567808378753000000 1567808378753274000
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1036-gcp
Version : #39-Ubuntu SMP Thu Jan 14 18:41:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 19.3.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | 2021-02-19T04:15:49Z | 2021-02-19T18:35:41Z | 2021-02-19T18:32:12Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 0 | 2021-02-19T04:17:04Z | 2021-02-19T15:45:38Z | 2021-02-19T14:22:19Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/39902 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39902/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39902/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39902/events | https://github.com/pandas-dev/pandas/pull/39902 | 811,832,014 | MDExOlB1bGxSZXF1ZXN0NTc2Mjk4OTQw | 39,902 | STYLE keep local imports relative | {
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} | [] | closed | false | null | [] | null | 1 | 2021-02-19T08:54:25Z | 2021-02-19T12:33:16Z | 2021-02-19T12:33:16Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
xref [this comment](https://github.com/pandas-dev/pandas/pull/39561#discussion_r575644482):
> > more generally, i like relative imports for same-directory as it clarified dependency structure
>
> +1, I also find this helpful. I think it should certainly be doable to have a check that relative imports are never outside the local directory
@jorisvandenbossche @jbrockmendel is this what you had in mind?
Like this, imports in the same folder are relative, and those outside are absolute.
So, for example, in `pandas/_testing/_io.py`:
- `from pandas.io.common import urlopen` stays absolute
- `from pandas._testing.contexts import ensure_clean` becomes `from .contexts import ensure_clean` | {
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] | open | false | null | [] | null | 1 | 2021-02-19T09:50:26Z | 2021-08-15T18:34:57Z | null | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
From [StackOverflow question posted by Usal](https://stackoverflow.com/q/66274713/12416453)
```python
df=pd.DataFrame({'Col1':[1, '-','-'], 'Col2':['-','-',2]})
df.sum(axis=1)
# 0 0.0
# 1 0.0
# 2 0.0
# dtype: float64
df.sum(axis=0)
# Series([], dtype: float64)
```
#### Problem description
Inconsistent output when same operation i.e. `df.sum` is applied over axis 0 and 1.
#### Expected Output
```
df.sum(axis=0)
# Col1 0.0
# Col2 0.0
# dtype: float64
df.sum(axis=1)
# 0 0.0
# 1 0.0
# 2 0.0
# dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
<pre><code>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-43-generic
Version : #49~20.04.1-Ubuntu SMP Fri Feb 5 09:57:56 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IN
LOCALE : en_IN.ISO8859-1
pandas : 1.2.2
numpy : 1.19.5
pytz : 2019.3
dateutil : 2.7.3
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</code></pre>
</details>
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} | 9 | 2021-02-19T10:19:36Z | 2021-04-01T07:15:40Z | 2021-04-01T07:15:11Z | MEMBER | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> from numpy import nan
>>>
>>>
>>> column_index = pd.MultiIndex.from_product([["sample1", "sample2"], [True], [True]],
... names=["Name", "Sample_column", "Write"])
>>> table = pd.DataFrame(data={"sample1": [100.0, nan, 100.0, 10.0],
... "sample2": [80.0, 20.0, 100.0, 100.0]})
>>> table.columns = column_index
>>>
>>> table2 = pd.DataFrame(data={"sample1": [100.0, nan, 100.0, 10.0],
... "sample2": [80.0, 20.0, 100.0, 100.0]},
... columns=column_index)
>>> table
Name sample1 sample2
Sample_column True True
Write True True
0 100.0 80.0
1 NaN 20.0
2 100.0 100.0
3 10.0 100.0
>>> table2
Empty DataFrame
Columns: [(sample1, True, True), (sample2, True, True)]
Index:
```
#### Problem description
If a MultiIndex is used to set the columns using the columns argument from the constructor, the MultiIndex is set correctly but the whole dataframe becomes empty. If the columns index is applied manually after construction by using df.columns, the dataframe does not lose its data. I think this is either a bug, or a clear warning or error message should be given why the dataframe becomes empty.
#### Expected Output
I expect the constructor to return a non-empty dataframe with the MultiIndex applied to the columns. The API reference mentions that is can be an 'Index or array-like', but I don't know if a MultiIndex is allowed.
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-58-generic
Version : #64-Ubuntu SMP Wed Dec 9 08:16:25 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | 2021-02-19T10:55:25Z | 2021-02-20T11:48:24Z | 2021-02-19T11:11:51Z | NONE | null | #### Location of the documentation
If you go to https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html with a current Google Chrome (Version 88.0.4324.150 (Official Build) (64-bit), Linux), you get this red page
that says that the site is dangerous. Upon further inspection, you come across [this entry of the transparency report](https://transparencyreport.google.com/safe-browsing/search?url=https:%2F%2Fpandas.pydata.org%2Fpandas-docs%2Fstable%2Freference%2Fapi%2Fpandas.DataFrame.drop.html&hl=en-US) which says:
> The site https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html contains harmful content, including pages that:
>
> * Install unwanted or malicious software on visitors’ computers
> * Contain unwanted or malicious software
If you tell Chrome that you understand the risks, then it displays the page but a dangerous icon is still being displayed at the URL box.
A similar thing happens with Firefox (`78.7.1esr`).
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- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
columns = ["type", "symbol", "Date", "Time", "TSNumber", "Price", "Quantity", "SettlementDate", "Yield", "AccruedInterest", "ConditionCodes", "QuotationBasis", "ExchangeRate", "TradeAttributes"]
df = pd.read_csv("20200803.csv",dtype = str,names=columns)
```
#### Problem description
Reading in a csv file with the following line
T,FMG,20200803,000000,10071068,,,20200804,,,XT,,,,
results in pandas ignoring the 4th column, and matches the current column to the next value (10071068) in that case.
Previous reports suggested setting the dtype to string (see https://stackoverflow.com/questions/13250046/how-to-keep-leading-zeros-in-a-column-when-reading-csv-with-pandas), which should be fine if the column is read in as "0000000", but apparently it is not. Am I missing something?
Current output:
-- | -- | -- | -- | --
71068 | FMG | 20200803 | 10071068 | 20200803.0 ...
#### Expected Output
-- | -- | -- | -- | --
71068 | FMG | 20200803 | 000000 | 10071068 | 20200803.0 ...
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-7642-generic
Version : #47~1612288990~20.04~b8113e7-Ubuntu SMP Wed Feb 3 02:25:36 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.3.3.post20210118
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : 0.5.0
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : 0.52.0
</details>
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} | 4 | 2021-02-19T13:39:51Z | 2021-02-20T18:16:14Z | 2021-02-20T18:16:14Z | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
type(pandas.Series({("a", 1): 1, ("a", 2): 2, ("b", 1): 3, ("b", 4): 4}).droplevel(level=1))
```
#### Problem description
Both the type hint for pandas.Series.droplevel() and the documentation say the function returns a DataFrame.
However, it clearly returns a Series.
While not a Bug per se, this does violate the Law of Least Suprise
#### Output of ``pd.show_versions()``
<details>
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104-microsoft-standard
Version : #1 SMP Wed Feb 19 06:37:35 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 0.17.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 39 | 2021-02-19T14:03:54Z | 2021-06-08T14:25:20Z | 2021-06-08T13:36:42Z | MEMBER | null | continuation of #36142
todo:
- [x] during the merge I removed the import gymnastics from `pandas/core/arrays/string_arrow.py`. We added since `ArrowStringDtype` should always be available. I think this is no longer the case and we want to move the import checks to `StringDtype`. I expect envs without pyarrow to fail for now.
- [x] `test_arrow_roundtrip` is failing. `StringDtype.__from_arrow__` needs updating.
- [x] address @jorisvandenbossche comments https://github.com/pandas-dev/pandas/pull/36142#discussion_r484213504 and https://github.com/pandas-dev/pandas/pull/36142#discussion_r484213103
- [x] some other test failures `AssertionError: assert 'unknown-array' == 'string'` | {
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] | closed | false | null | [] | null | 1 | 2021-02-19T14:10:30Z | 2021-02-19T14:12:33Z | 2021-02-19T14:12:22Z | NONE | null | Not sure if this is the right repo to report this, but Chrome browser is now displaying a scary red warning when I try to visit Pandas docs on https://pandas.pydata.org/.
Chrome version is 88.0.4324.182. | {
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https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html
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] | open | false | null | [] | null | 5 | 2021-02-19T14:49:11Z | 2021-10-26T18:39:05Z | null | CONTRIBUTOR | null | - [ X] I have checked that this issue has not already been reported.
- [ X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
If you run the following in a Jupyter cell:
```python
import pandas as pd
pd.options.display.latex.repr = True
d = {'one' : [1., 2., 3., 4.],
'two' : [4., 3., 2., 1.]}
df = pd.DataFrame(d)
df
```
The result is the HTML repr, as usual. But if you select a column, the Series shows the LaTeX repr, as it should.
```
df['one']
```
But `DataFrame` has `_repr_latex_`, because this works:
```
df._repr_latex_()
```
So it seems like DataFrame is ignoring the `display.latex.repr` option.
#### Problem description
When `display.latex.repr` is True, DataFrames should get represented in LaTeX, not HTML.
#### Expected Output
```
'\\begin{tabular}{lrr}\n\\toprule\n{} & one & two \\\\\n\\midrule\n0 & 1.0 & 4.0 \\\\\n1 & 2.0 & 3.0 \\\\\n2 & 3.0 & 2.0 \\\\\n3 & 4.0 & 1.0 \\\\\n\\bottomrule\n\\end{tabular}\n'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-72-generic
Version : #81~16.04.1-Ubuntu SMP Tue Nov 26 16:34:21 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.2
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/39912 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39912/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39912/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39912/events | https://github.com/pandas-dev/pandas/issues/39912 | 812,099,501 | MDU6SXNzdWU4MTIwOTk1MDE= | 39,912 | BUG: pd.io.sql.get_schema function converts/Shows boolean Data types as a int Datatype | {
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] | closed | false | null | [] | null | 0 | 2021-02-19T15:08:58Z | 2021-02-20T04:18:47Z | 2021-02-20T04:16:09Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1037-aws
Version : #39~18.04.1-Ubuntu SMP Fri Jan 15 02:48:42 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 39.0.1
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.50.1
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] | open | false | null | [] | null | 2 | 2021-02-19T17:28:42Z | 2021-07-11T18:05:34Z | null | NONE | null | #### Is your feature request related to a problem?
I see many APIs return results in the form:
```json
{
"results": [
{"a" :1,
"b":2
},
{
"a" :3,
"b":4
}
]
}
```
This format isn't directly supported by pandas. The data is in the "records" orient, but there is an extra layer. Currently to load this file I am using the requests module to load from https, then using the json module to strip out the outer layer, then feeding this data to pd.read_json as text. This feels like overkill, since pandas can read from https, but for this format (which is common for APIs) I need multiple other packages and lines of code.
This will change a three import, multi-line issue into a single import, single line solution.
#### Describe the solution you'd like
While I initially describe this as a new orient, I don't think that is the best way to implement this. I believe the `read_json` function should have a new parameter (such as "strip_layer") which will be the value of that outer layer. In the example above that would be "results". I make this suggestion as what is inside the outer layer could be several different orients, so we need to leave that as a possibility. This is something that happens first, then the data is processed.
#### API breaking implications
Need to consider what this means for chunking.
#### Additional context
My current code:
```python
import pandas as pd
import requests
import json
data = requests.get(url).json()
data = data["results"]
data = json.dumps(data)
data = pd.read_json(data)
```
versus my desired code with this improvement:
```python
import pandas as pd
data = pd.read_json(url, strip_layer="results")
```
Might I suggest this gets added to the IO Method Robustness/Input Types Project? | {
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] | closed | false | null | [] | null | 11 | 2021-02-19T17:39:36Z | 2021-08-15T18:42:15Z | 2021-08-15T18:42:14Z | NONE | null | #### Add multi-layered vector support
When working on a large panadas dataframe, I tried to use the iloc method on the dataframe with a list of lists index query. I thought that the behavior of iloc with a 1d array would be extended to 2d arrays, and it would return a 2d array of the query results. This would allow for lists of lists to be operated on inside the dataframe.
#### Extending the behavior of iloc to multidimensional indexes
I think that the way that iloc handles 1d arrays should be extended to 2d and possibly even higher dimensions to allow for efficient loopless code to be written. For example, passing iloc a 2d array of indices should have it return 2d array of the results.
#### API breaking implications
This shouldn't break any existing code, or add any new arguments to iloc, just make the backend handle multidimensional arrays and return a multidimensional array as necessary.
#### Alternatives
A loop can just be made in python to achieve the same effect but will be significantly slower. While my dataset could probably be operated on with just python and some basic patience, the most ideal solution, as far as I can tell, would be this.
#### Example
This is what could be an implementation of this could look like.
```
indexArray = [[0, 3, 5, 7, 9], [1, 4, 6, 8]]
2d_result = df.iloc[indexArray]
```
2d_result would be an array with the shape of the indexArray populated by results from df. | {
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] | open | false | null | [] | null | 7 | 2021-02-19T18:04:54Z | 2021-08-15T18:42:58Z | null | NONE | null | #### Is your feature request related to a problem?
When using `df.explode`, I'd expect that a list-like `L` gets transformed into `len(L)` rows and a scalar into 1 row. While documented, the current behavior of turning empty list-likes into 1 row containing NaN instead of 0 rows is counterintuitive.
#### Describe the solution you'd like
Ideally `df.explode` would transform empty list-likes into 0 rows, or, to maintain backwards compatibility, introduce an argument `collapse_empty=False` (or similar -- maybe `implode_empty` in keeping with the theme) that I could set to `True`.
#### API breaking implications
If `collapse_empty=False` by default then there would be no API breaking changes.
#### Describe alternatives you've considered
Carefully using `df.dropna` after `df.explode` to remove any null values I don't want while preserving null values that had belonged to lists prior to the explode.
#### Additional context
Using the example in the docs here: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.explode.html
```python
>>> df = pd.DataFrame({'A': [[1, 2, 3], 'foo', [], [3, 4]], 'B': 1})
>>> df
A B
0 [1, 2, 3] 1
1 foo 1
2 [] 1
3 [3, 4] 1
>>> df.explode('A', collapse_empty=True)
A B
0 1 1
0 2 1
0 3 1
1 foo 1
3 3 1
3 4 1
```
I believe that the change would require adding a corresponding argument to [`reshape.explode`](https://github.com/pandas-dev/pandas/blob/75609da1dfcf136f9a6e2f37eff5809cacde2925/pandas/_libs/reshape.pyx#L103) and then using that argument in the length checks in lines [129](https://github.com/pandas-dev/pandas/blob/75609da1dfcf136f9a6e2f37eff5809cacde2925/pandas/_libs/reshape.pyx#L129) and [143](https://github.com/pandas-dev/pandas/blob/75609da1dfcf136f9a6e2f37eff5809cacde2925/pandas/_libs/reshape.pyx#L143). Something like this:
```python
if len(v):
...
elif not collapse_empty:
# empty list-like, use a nan marker
...
``` | {
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} | 7 | 2021-02-19T20:02:42Z | 2021-02-28T15:59:21Z | 2021-02-28T08:38:16Z | CONTRIBUTOR | null | - [ ] closes #38749
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 2 | 2021-02-19T20:12:35Z | 2021-03-01T15:46:49Z | 2021-03-01T13:56:08Z | MEMBER | null | The idea here is to collect benchmarks in such a way that we can say "This PR doesn't change code in files [...] -> We can skip running asvs in files [...]". (putting together a POC script to automate a bit of this)
There is a cost to refactoring benchmarks, since we have to re-run the new benchmarks on older commits (and im not sure if that happens automatically or if some human intervention is needed cc @TomAugspurger ?). On the flip side, this is the kind of problem that we can throw hardware at, so I don't worry about it that much. Still, the bar for merging is higher than it would be for non-benchmark refactors.
With that in mind, the other refactors I'm looking at:
1) We have benchmarks for e.g. `isin` scattered around, benchmarking `[algos.isin(x, y), pd.array(x).isin(y), Index(s).isin(y), Series(x).isin(y)]`. I'd like to collect these in one place and parametrize them.
2) Similar for Index methods, we have a lot of similar benchmarks for different index subclasses that id like to collect+parametrize.
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} | 4 | 2021-02-19T20:35:32Z | 2021-02-19T21:59:02Z | 2021-02-19T21:58:44Z | CONTRIBUTOR | null | I am getting "site contains harmful programs' error here:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html
> The site ahead contains harmful programs
> Attackers on pandas.pydata.org might attempt to trick you into installing programs that harm your browsing experience (for example, by changing your homepage or showing extra ads on sites you visit). Learn more | {
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} | https://api.github.com/repos/pandas-dev/pandas/issues/39918/timeline | null | null | null |
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